1. Field of the Invention
The present invention relates to learning of parameters for classifying a pattern of an input signal and pattern classification using this learning in recognition of images and voices.
2. Description of the Related Art
Many methods have been suggested as a pattern classification method for classifying input data into a predetermined class in optical character recognition or automatic voice recognition. Also, under present circumstances, various new methods are suggested for the purpose of an increase in processing speed and enhancement of classification accuracy.
For example, a high-speed and high-accuracy pattern classification method has been suggested based on a combination of a learning method by AbaBoost and a cascade classification method of a weak discrimination method, which enables very high speed operation (see P. Viola, M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 1, pp. 511-518, December 2001).
Also, for the purpose of further increasing speed and accuracy in the pattern classification method, a method of introducing a boosting method has been suggested (see R. O. Duda, P. E. Hart, D. G. Stork, “Pattern Classification Second Edition”, ShinGijutsu Communications K.K. pp. 478-482, July 2001). Other methods include a real AbaBoost method (see R. E. Schapire, Y. Singer, “Improved Boosting Algorithms Using Confidence-rated Predictions”, Machine Learning, Vol. 37, pp. 297-336, December 1999) and a nested cascade classification method (see Rou Sekou, Takayoshi Yamashita, Takuya Okamoto, Masahito Kawade “Fast Omni-Directional Face Detection”, MURU 2004, Vol. 2, pp. 271-276, July 2004).
Also, a pattern recognition method called a support vector machine (SVM) capable of configuring a nonlinear classification function is known (see Kouji Tsuda “Technical survey: Overview of Support Vector Machine”, The Journal of the Institute of Electronics, Information, and Communication Engineers, Vol. 83, pp. 460-466, June 2000).
In such a background of higher performance of processing apparatuses, a technique enabling real-time pattern classification of high speed and high accuracy has been required.